Real-time monitoring system for early prediction of heart disease using Internet of Things

The diagnosis of heart disease is found to be a serious concern, so the diagnosis has to be done remotely and regularly to take the prior action. In the present years, the diagnosis of heart disease has become a key research area for researchers and many models have been proposed in recent years. The diagnosis of heart disease can be done using optimization algorithms, and it provides results with good efficiency. The main objective of this paper is to propose a hybrid fuzzy-based decision tree algorithm for the process of prediction of heart disease at an early stage through the continuous and remote patient monitoring system. The results obtained from the proposed algorithm are compared with the various number of classifier algorithms like decision tree J48, naïve Bayes, GA with FCM, KNN with NB, ANN, SVM with fuzzy in which the proposed HFDT algorithm provides better accuracy of 98.30%. From the above-obtained results, the proposed hybrid fuzzy-based decision tree algorithm efficiently predicts heart disease compared to the other classifier algorithms in the literature. The proposed work is implemented in the MATLAB environment using the heart disease dataset.